Guest post from Dr. Gregory Bowman, UC Berkeley
We’ve been making a lot of progress with developing Markov state model (MSM) methods for analyzing the data we generate with the help of the FAH community. For those of you with a theory background, MSMs are just discrete-time master equation models. For everyone else, MSMs are a way of describing the conformational space a protein (or other biomolecule for that matter) explores as a set of states (i.e. distinct structures) and the transition rates between them. Much of the theory underlying these methods is quite old but their use has been limited by the challenges inherent to identifying a reasonable set of states.
During my time in the Pande lab, I worked with Xuhui Huang (now at the Hong Kong University of Science and Technology) to develop new methods for building MSMs from the large data sets we generate with FAH. Together, we started an open source software package called MSMBuilder (here) to automate the process of building MSMs. Now a number of more recent additions to the Pande lab are helping Xuhui, Vijay, and me in continuing to develop the software.
As we just released an update to MSMBuilder, I was looking back at some of our user statistics and was pleased to see how quickly our project is gaining traction. Since its initial release in 2009, there have been over 1,600 unique downloads of MSMBuilder. One cute feature of our webpage—provided by the SimTk software consortium at Stanford—is that you can go look where all of our users are (here). Its fun to see that MSMBuilder is being used on 5 continents. Maybe most importantly, MSMBuilder has been used in at least 40 publications to date. MSMBuilder is coming up at conferences with increasing frequency too, so I look forward to reporting back on our growth in another year or so.